vignettes/regression.R

unscale<-function(X) {
  mu_X<-attr(X,"scaled:center");sigma_X<-attr(X,"scaled:scale")
  return(t(apply(X, 1, function(r)r*mu_X + attr(X, 'scaled:center'))))
}

standardize_result<-function(result,X,Y,standardize,standardize_response) {
  L<-length(result[[1]])
  if(standardize) {
    main_effects<-result[[1]]; beta_main<-result[[2]]; intr_effects<-result[[3]]; beta_intr<-result[[4]]; intercept<-result[[6]];
    mu_X<-attr(X,"scaled:center");sigma_X<-attr(X,"scaled:scale")
    X<-unscale(X)

    for(l in 1:L) {
      for(i in 1:length(main_effects[[l]])) {
        beta_main[[l]][i]<-beta_main[[l]][i]/sigma_X[main_effects[[l]][i]]
        intercept[l]<-intercept[l]-beta_main[[l]][i]*mu_X[main_effects[[l]][i]]
      }
      for(i in 1:length(beta_intr[[l]])) {
        variable<-X[,intr_effects[[l]][1,i]]*X[,intr_effects[[l]][2,i]]
        mu_XX<-mean(variable)
        sigma_XX<-stats::sd(variable)
        beta_intr[[l]][i]<-beta_intr[[l]][i]/sigma_XX
        intercept[l]<-intercept[l]-beta_intr[[l]][i]*mu_XX
      }
    }
  }
  if(standardize_response) {
    mu_Y<-attr(Y,"scaled:center"); sigma_Y<-attr(Y,"scaled:scale")
    for(l in 1:L) {
      beta_main[[l]]<-beta_main[[l]]*sigma_Y
      beta_intr[[l]]<-beta_intr[[l]]*sigma_Y
      intercept[l]<-sigma_Y*intercept[l]+mu_Y
    }
  }
  return(result)
}

#' Elasticnet with interactions (glmnet)
#' @param X A matrix.
#' @param Y A vector.
#' @param L An integer indicating how many projection steps are performed.
#' @param standardize A boolean indicating if X should be scaled and centered.
#' @param standardize_response A boolean indicating if Y should be scaled and centered.
#' @param n_lambda A natural number indicating how long the path of lambdas should be.
#' @param lambdas A vector of decreasing real numbers containing user specified values of lambda.
#' @param alpha  A real number between 0 and 1 (the elastic net parameter)
#' @return \code{N} strongest interactions (of type \code{type}) between \code{X} and \code{Y} after \code{L} projections.
#' @examples
#' n<-300
#' p<-1000
#' #build matrix of predictors
#' X<-matrix(rnorm(n*p),n,p)
#' #build a main effect and an interaction into Y
#' Y<-4*X[,1]*X[,2]-5*X[,4]+rnorm(n)
#' result<-xyz_regression(X,Y,n_lambda=10,alpha=0.9,L=10)
#' #print the result
#' print(result)
#' #plot the result
#' plot(result)
#' @export
#' @useDynLib xyz
#' @importFrom Rcpp sourceCpp
xyz_regression<-function(X,Y,lambdas=NULL,n_lambda=10,alpha=0.9,L=10,standardize=TRUE,
                         standardize_response=TRUE) {
  L<-round(L)
  if(L < 1) {
    stop("Number of runs has to be at least 1.")
  }
  if(L > 100) {
    warning("You choose very high number of runs.")
  }
  if(n_lambda < 1) {
    stop("Length of lambda sequence cannot be smaller than 1.")
  }

  #do translating checks here
  if(!is.matrix(X)) {
    X<-as.matrix(X)
    if(!is.matrix(X)) {
      stop("X cannot be coerced to a matrix.")
    }
  }

  n<-dim(X)[1]
  p<-dim(X)[2]
  if(!is.vector(Y)) {
    stop("Y has to be a vector.")
  }
  if(length(Y) != n) {
    stop("Y and X have to have the same number of rows")
  }
  if(n < 10) {
    stop(paste("You have ",n," samples. The number of samples should at least be 10.",sep=""))
  }
  if(standardize) {
    X<-scale(X)
  }
  if(standardize_response) {
    Y<-scale(Y)
  }
  if(is.null(lambdas)) {
    lambdas<-rep(-1,n_lambda)
  }
  max_main_effects<-100
  max_interaction_effects<-20
  result<-gaussiglmnet(X, Y, lambdas, alpha,standardize, max_main_effects, max_interaction_effects, 2, L)
  L<-length(result[[1]])
  for(i in 1:L) {
    result[[1]][[i]]<-result[[1]][[i]]+1
    result[[3]][[i]]<-result[[3]][[i]]+1
  }
  result<-standardize_result(result,X,Y,standardize,standardize_response)
  class(result)<-"xyz_regression_result"
  return(result)
  stop("You reached the end of the function and it won't return anything. This is not good and u should be ashamed.")
}

#' @export
summary.xyz_regression_result<-function(object,...) {
  x<-object
  l_main_1<-length(x[[1]])
  l_intr<-dim(x[[3]][[l_main_1]])[2]
  l_main<-length(x[[1]][[l_main_1]])
  if(l_main==1 & x[[2]][[l_main_1]][1]==0) {
    l_main = 0
  }
  if(l_intr==1 & x[[4]][[l_main_1]][1]==0) {
    l_intr = 0
  }
  output<-paste("Discovered main effects: ",l_main," Discovered interaction effects: ",l_intr,sep="")
  return(output)
}

#' @export
print.xyz_regression_result<-function(x,...,whichlambda=-1) {
  lambda_sequence<-round(x[[5]],digits=5)
  cat("Lambda sequence:\n")
  cat(paste("lambda",1:length(lambda_sequence),"=",lambda_sequence,"\n",sep=""))
  l_main_1<-length(x[[1]])
  l_intr<-dim(x[[3]][[l_main_1]])[2]
  l_main<-length(x[[1]][[l_main_1]])
  if(l_main==1 & x[[2]][[l_main_1]][1]==0) {
    l_main = 0

  }
  if(l_intr==1 & x[[4]][[l_main_1]][1]==0) {
    l_intr = 0
  }
  cat(paste("Discovered main effects: ",l_main," Discovered interaction effects: ",l_intr,"\n",sep=""))
  l<-whichlambda
  cat("Model parameters:\n")
  cat("intercept: ",x[[6]][1],"\n",sep="")
  if(l_main + l_intr > 0) {
    if(l<1) {
      l<-length(lambda_sequence)
    } else {
      l <- round(l)
      if(l < 1 | l > length(lambda_sequence)) {
        l<-length(lambda_sequence)
      }
    }
    cat(paste("Printing effects for lambda",l,"=",lambda_sequence[l],"\n",sep=""))
  }
  if(l_main > 0) {
    cat("Main effects:\n")
    ord<-order(abs(x[[2]][[l]]),decreasing=TRUE)
    x[[1]][[l]]<-x[[1]][[l]][ord]
    x[[2]][[l]]<-x[[2]][[l]][ord]
    for(i in 1:length(x[[1]][[l]])) {
      cat("Main effect: ",x[[1]][[l]][i]," coefficient: ",x[[2]][[l]][i],"\n",sep="")
    }
  }
  if(l_intr > 0) {
    cat("Interaction effect:\n")
    ord<-order(abs(x[[4]][[l]]),decreasing=TRUE)
    x[[3]][[l]]<-matrix(x[[3]][[l]][,ord],2,length(x[[4]][[l]]))
    x[[4]][[l]]<-x[[4]][[l]][ord]
    for(i in 1:length(x[[4]][[l]])) {
      cat("Interaction effect: (",x[[3]][[l]][1,i],",",x[[3]][[l]][2,i],") coefficient: ",x[[4]][[l]][i],"\n",sep = "")
    }
  }
}

#' @importFrom graphics matplot legend
#' @export
plot.xyz_regression_result<-function(x,...) {
  if( length(x) != 6 ) {
    stop("fit has parts missing")
  }
  main_effects<-x[[1]]
  main_coefs<-x[[2]]
  intr_effects<-x[[3]]
  intr_coefs<-x[[4]]
  lambdas<-x[[5]]
  path_length<-length(lambdas)

  l<-length(x[[1]])

  #find all main and intr effects
  all_main <- unique(unlist(main_effects))
  all_intr <- matrix(unlist(intr_effects),ncol=2,byrow=TRUE)
  all_intr <- all_intr[!duplicated(all_intr),]

  nr_main<-length(all_main)
  nr_intr<-length(all_intr)/2

  main_path<-list()
  intr_path<-list()
  for(i in 1:nr_main) {
    temp<-rep(0,path_length)
    for(j in 1:path_length) {
      temp_pos<- -1
      for(k in 1:length(main_effects[[j]])) {
        if(main_effects[[j]][k] == all_main[i]) {
          temp_pos<-k
        }
      }
      if(temp_pos>0) {
        temp[j] <- main_coefs[[j]][temp_pos]
      } else {
        temp[j] <-0
      }
    }
    main_path[[i]]<-temp
  }
  if(nr_intr>1) {
    for(i in 1:nr_intr) {
      temp<-rep(0,path_length)
      for(j in 1:path_length) {
        temp_pos<- -1
        intr_effects[[j]]<-matrix(intr_effects[[j]],2,length(intr_coefs[[j]]))
        for(k in 1:dim(intr_effects[[j]])[2]) {

          if(intr_effects[[j]][1,k] == all_intr[i,1] && intr_effects[[j]][2,k] == all_intr[i,2]) {
            temp_pos<-k
          }
          if(intr_effects[[j]][2,k] == all_intr[i,1] && intr_effects[[j]][1,k] == all_intr[i,2]) {
            temp_pos<-k
          }
        }
        if(temp_pos>0) {
          temp[j] <- intr_coefs[[j]][temp_pos]
        } else {
          temp[j] <-0
        }
      }
      intr_path[[i]]<-temp
    }
  } else {
    intr_path[[1]]<- unlist(intr_coefs)
  }
  all_paths<-matrix(c(unlist(main_path),unlist(intr_path)),nrow=nr_main+nr_intr,byrow=TRUE)

  beta_norm<-colSums(abs(all_paths))

  colors_plot<-c(rep("blue",nr_main),rep("red",nr_intr))
  matplot(beta_norm,t(all_paths),type="l",col=colors_plot,lty=1,lwd=2,ylab="coefficients",xlab="L1 Norm")
  legend("topleft",legend=c("main effects","interaction effects"),lty=c(1,1),col=c("blue","red"))
}

#' @export
predict.xyz_regression_result<-function(object,newdata,...) {
  l<-length(object[[1]])
  main_effects<-c(object[[1]][[l]],1)
  beta_main<-c(object[[2]][[l]],0)
  intr_effects<-cbind(object[[3]][[l]],c(1,1))
  beta_intr<-c(object[[4]][[l]],0)
  intercept<-object[[6]][l]
  Y_pred<-intercept+newdata[,main_effects]%*%beta_main+(newdata[,intr_effects[1,]]*newdata[,intr_effects[2,]])%*%beta_intr
  return(Y_pred)
}

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xyz documentation built on May 30, 2017, 1:52 a.m.